scholarly journals Red Deer and Simulation Annealing Optimization Algorithm-based Energy Efficient Clustering Protocol for Improved lifetime expectancy in Wireless Sensor Networks (WSNs)

2021 ◽  
Author(s):  
Rajeswarappa Govardanagiri ◽  
Vasundra S
Keyword(s):  
Energy Efficiency ◽  
Network Lifetime ◽  
Optimization Algorithm ◽  
Energy Efficient ◽  
Red Deer ◽  
Energy Stability ◽  
Wireless Sensor ◽  
Clustering Protocol ◽  
Energy Efficient Clustering ◽  
Lifetime Expectancy

Abstract Energy stability is considered as the core challenge of Wireless Sensor Networks (WSNs) as it is the main factor that contributes towards improved network lifetime expectancy. Clustering with maximized energy efficiency is determined to be the well documented NP hard optimization solution that has the possibility of resulting in prolonged network lifetime. Diversified number of computational approaches that includes nature inspired metaheuristic algorithms, reinforcement schemes and evolutionary algorithms were used for attaining efficient clustering process in WSNs. In this paper, Red Deer and Simulation Annealing Optimization Algorithm-based Energy Efficient Clustering Protocol (RDSAOA-EECP) is proposed for improving lifetime expectancy and energy stability in WSNs. This clustering protocol is proposed with the global optimization capability of the red deer optimization algorithm and local optimization potential of simulation annealing. In specific, RDA is used for diversification and Simulated Annealing (SA) for intensification in order to establish balance between them during the clustering process in order to sustain energy stability. This RDSAOA-EECP protocol is proposed with SA as the search tool that prevents the phases of fighting and roaring phases of RDA involved in the intensification process. Moreover, this clustering protocol inherits the potentialities of the proposed metaheuristic properties in order to achieve optimal cluster heads together with the optimal base station location for the objective of enhancing energy efficiency. The simulation results of the proposed RDSAOA-EECP protocol guaranteed its potentiality over the baseline clustering approaches in improving network lifetime, throughput, packet deliver rate with minimized network latency and energy consumptions independent to the homogeneous and heterogeneous sensor network setup considered for implementation.

An Energy Efficient Clustering Protocol using Enhanced Rain Optimization Algorithm in Mobile Adhoc Networks

2021 ◽  
pp. 85-94
Author(s):  
Mohamed Elhoseny ◽  
◽  
◽  
X. Yuan
Keyword(s):  
Energy Efficiency ◽  
Optimization Algorithm ◽  
Energy Efficient ◽  
Ad Hoc ◽  
Superior Performance ◽  
Clustering Protocol ◽  
New Energy ◽  
Hoc Networks ◽  
Experimental Values ◽  
Energy Efficient Clustering

Energy efficiency is a significant challenge in mobile ad hoc networks (MANETs) design where the nodes move randomly with limited energy, leading to acceptable topology modifications. Clustering is a widely applied technique to accomplish energy efficiency in MANET. Therefore, this paper designs a new energy-efficient clustering protocol using an enhanced rain optimization algorithm (EECP-EROA) for MANET. The EROA technique is derived by integrating the Levy flight concept to the ROA to enhance global exploration abilities. In addition, the EECP-EROA technique intends to proficiently select CHs and the nearby nodes linked to the CH to generate clusters. Moreover, the EECP-EROA technique has derived an objective function with different input parameters. To showcase the superior performance of the EECP-EROA technique, a brief set of simulations takes place, and the results are inspected under varying aspects. The experimental values pointed out the betterment of the EECP-EROA technique over the other methods.

scholarly journals Threshold Balanced Sampled DEEC Model for Heterogeneous Wireless Sensor Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Sercan Vançin ◽  
Ebubekir Erdem
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Energy Efficient ◽  
Wireless Sensor ◽  
Distributed Energy ◽  
Clustering Method ◽  
Clustering Protocol ◽  
Energy Efficient Clustering

Due to the restricted hardware resources of the sensor nodes, modelling and designing energy efficient routing methods to increase the overall network lifetime have become one of the most significant strategies in wireless sensor networks (WSNs). Cluster-based heterogeneous routing protocols, a popular part of routing technology, have proven effective in management of topology, energy consumption, data collection or fusion, reliability, or stability in a distributed sensor network. In this article, an energy efficient three-level heterogeneous clustering method (DEEC) based distributed energy efficient clustering protocol named TBSDEEC (Threshold balanced sampled DEEC) is proposed. Contrary to most other studies, this study considers the effect of the threshold balanced sampled in the energy consumption model. Our model is compared with the DEEC, EDEEC (Enhanced Distributed Energy Efficient Clustering Protocol), and EDDEEC (Enhanced Developed Distributed Energy Efficient Clustering Protocol) using MATLAB as two different scenarios based on quality metrics, including living nodes on the network, network efficiency, energy consumption, number of packets received by base station (BS), and average latency. After, our new method is compared with artificial bee colony optimization (ABCO) algorithm and energy harvesting WSN (EH-WSN) clustering method. Simulation results demonstrate that the proposed model is more efficient than the other protocols and significantly increases the sensor network lifetime.

scholarly journals Energy Efficient Clustering Protocol to Enhance Performance of Heterogeneous Wireless Sensor Network: EECPEP-HWSN

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Santosh V. Purkar ◽  
R. S. Deshpande
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Energy Efficient ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Performance Parameters ◽  
Clustering Protocol ◽  
Heterogeneous Wireless Sensor Network ◽  
Energy Efficient Clustering

Heterogeneous wireless sensor network (HWSN) fulfills the requirements of researchers in the design of real life application to resolve the issues of unattended problem. But, the main constraint faced by researchers is the energy source available with sensor nodes. To prolong the life of sensor nodes and thus HWSN, it is necessary to design energy efficient operational schemes. One of the most suitable approaches to enhance energy efficiency is the clustering scheme, which enhances the performance parameters of WSN. A novel solution proposed in this article is to design an energy efficient clustering protocol for HWSN, to enhance performance parameters by EECPEP-HWSN. The proposed protocol is designed with three level nodes namely normal, advanced, and super, respectively. In the clustering process, for selection of cluster head we consider different parameters available with sensor nodes at run time that is, initial energy, hop count, and residual energy. This protocol enhances the energy efficiency of HWSN and hence improves energy remaining in the network, stability, lifetime, and hence throughput. It has been found that the proposed protocol outperforms than existing well-known LEACH, DEEC, and SEP with about 188, 150, and 141 percent respectively.

scholarly journals EECDA: Energy Efficient Clustering and Data Aggregation Protocol for Heterogeneous Wireless Sensor Networks

2011 ◽  
Vol 6 (1) ◽  
pp. 113 ◽  
Author(s):  
Dilip Kumar ◽  
Trilok C. Aseri ◽  
R.B. Patel
Keyword(s):  
Energy Efficiency ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Network Lifetime ◽  
Data Aggregation ◽  
Energy Efficient ◽  
Data Gathering ◽  
Wireless Sensor ◽  
Energy Efficient Clustering

In recent years, energy efficiency and data gathering is a major concern in many applications of Wireless Sensor Networks (WSNs). One of the important issues in WSNs is how to save the energy consumption for prolonging the network lifetime. For this purpose, many novel innovative techniques are required to improve the energy efficiency and lifetime of the network. In this paper, we propose a novel Energy Efficient Clustering and Data Aggregation (EECDA) protocol for the heterogeneous WSNs which combines the ideas of energy efficient cluster based routing and data aggregation to achieve a better performance in terms of lifetime and stability. EECDA protocol includes a novel cluster head election technique and a path would be selected with maximum sum of energy residues for data transmission instead of the path with minimum energy consumption. Simulation results show that EECDA balances the energy consumption and prolongs the network lifetime by a factor of 51%, 35% and 10% when compared with Low-Energy Adaptive Clustering Hierarchy (LEACH), Energy Efficient Hierarchical Clustering Algorithm (EEHCA) and Effective Data Gathering Algorithm (EDGA), respectively.

Modified Grey Wolf Optimization with Levy Flights Based Energy Efficient Clustering Technique in Wireless Sensor Networks

2020 ◽  
Vol 17 (12) ◽  
pp. 5429-5437
Author(s):  
R. Sathiya Priya ◽  
K. Arutchelvan ◽  
C. Bhuvaneswari
Keyword(s):  
Energy Efficiency ◽  
Network Lifetime ◽  
Energy Efficient ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Clustering Technique ◽  
Cluster Heads ◽  
Energy Efficient Clustering

Wireless Sensor Network (WSN) comprises a set of inexpensive, compact and battery powered sensor nodes, deployed in the sensing region. WSN is highly useful for data gathering and tracking applications. Owing to the battery powered nature of sensor nodes, energy efficiency remains as a crucial design issue. Earlier works reported that clustering is considered as an energy efficient technique and effective selection of cluster heads (CHs) remains a major issue in WSN. Since clustering process is considered as an NP hard problem, optimization algorithms are employed to resolve it. This paper develops a new energy efficient clustering technique using Modified Grey Wolf Optimization with Levy Flights (MGWO-LF) for WSN. The proposed MGWO-LF algorithm incorporates the levy flight (LF) mechanism into the hunting phase of traditional GWO algorithm to avoid local optima problem. The proposed model has the ability of proficiently selecting the cluster heads (CHs), achieves energy efficiency and maximum network lifetime. The detailed simulation analysis ensured that the MGWO-LF algorithm has prolonged the network lifetime in a considerable way.

Enhanced Adaptive Distributed Energy-Efficient Clustering (EADEEC) for Wireless Sensor Networks

2020 ◽  
Vol 13 (2) ◽  
pp. 168-172
Author(s):  
Ravi Kumar Poluru ◽  
M. Praveen Kumar Reddy ◽  
Syed Muzamil Basha ◽  
Rizwan Patan ◽  
Suresh Kallam
Keyword(s):  
Network Lifetime ◽  
Routing Protocols ◽  
Energy Efficient ◽  
High Energy ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Distributed Energy ◽  
Stability Period ◽  
Energy Efficient Clustering

Background:Recently Wireless Sensor Network (WSN) is a composed of a full number of arbitrarily dispensed energy-constrained sensor nodes. The sensor nodes help in sensing the data and then it will transmit it to sink. The Base station will produce a significant amount of energy while accessing the sensing data and transmitting data. High energy is required to move towards base station when sensing and transmitting data. WSN possesses significant challenges like saving energy and extending network lifetime. In WSN the most research goals in routing protocols such as robustness, energy efficiency, high reliability, network lifetime, fault tolerance, deployment of nodes and latency. Most of the routing protocols are based upon clustering has been proposed using heterogeneity. For optimizing energy consumption in WSN, a vital technique referred to as clustering.Methods:To improve the lifetime of network and stability we have proposed an Enhanced Adaptive Distributed Energy-Efficient Clustering (EADEEC).Results:In simulation results describes the protocol performs better regarding network lifetime and packet delivery capacity compared to EEDEC and DEEC algorithm. Stability period and network lifetime are improved in EADEEC compare to DEEC and EDEEC.Conclusion:The EADEEC is overall Lifetime of a cluster is improved to perform the network operation: Data transfer, Node Lifetime and stability period of the cluster. EADEEC protocol evidently tells that it improved the throughput, extended the lifetime of network, longevity, and stability compared with DEEC and EDEEC.

Energy Efficient Clustering and Routing Algorithm for WSN

2019 ◽  
Vol 12 ◽  
Author(s):  
Mohit Kumar ◽  
Sonu Mittal ◽  
Md. Amir Khusru Akhtar
Keyword(s):  
Energy Consumption ◽  
Network Lifetime ◽  
Energy Efficient ◽  
Hot Spot ◽  
Routing Algorithm ◽  
Relay Node ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Energy Efficient Clustering ◽  
Hot Spot Problem

Background: This paper presents a novel Energy Efficient Clustering and Routing Algorithm (EECRA) for WSN. It is a clustering-based algorithm that minimizes energy dissipation in wireless sensor networks. The proposed algorithm takes into consideration energy conservation of the nodes through its inherent architecture and load balancing technique. In the proposed algorithm the role of inter-cluster transmission is not performed by gateways instead a chosen member node of respective cluster is responsible for data forwarding to another cluster or directly to the sink. Our algorithm eases out the load of the gateways by distributing the transmission load among chosen sensor node which acts as a relay node for inter-cluster communication for that round. Grievous simulations show that EECRA is better than PBCA and other algorithms in terms of energy consumption per round and network lifetime. Objective: The objective of this research lies in its inherent architecture and load balancing technique. The sole purpose of this clustering-based algorithm is that it minimizes energy dissipation in wireless sensor networks. Method: This algorithm is tested with 100 sensor nodes and 10 gateways deployed in the target area of 300m × 300m. The round assumed in this simulation is same as in LEACH. The performance metrics used for comparisons are (a) network lifetime of gateways and (b) energy consumption per round by gateways. Our algorithm gives superior result compared to LBC, EELBCA and PBCA. Fig 6 and Fig 7 shows the comparison between the algorithms. Results: The simulation was performed on MATLAB version R2012b. The performance of EECRA is compared with some existing algorithms like PBCA, EELBCA and LBCA. The comparative analysis shows that the proposed algorithm outperforms the other existing algorithms in terms of network lifetime and energy consumption. Conclusion: The novelty of this algorithm lies in the fact that the gateways are not responsible for inter-cluster forwarding, instead some sensor nodes are chosen in every cluster based on some cost function and they act as a relay node for data forwarding. Note the algorithm does not address the hot-spot problem. Our next endeavor will be to design an algorithm with consideration of hot-spot problem.

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